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Enregistrement W2040161818 · doi:10.1097/acm.0b013e318166a733

Teaching and Learning Moments

2008· article· en· W2040161818 sur OpenAlexfundno aff
José E. Rodríguez, Ronald Delphin

Notice bibliographique

RevueAcademic Medicine · 2008
Typearticle
Langueen
DomaineMedicine
ThématiqueInnovations in Medical Education
Établissements canadiensnon disponible
Organismes subventionnairesFaculty of Medicine and Health, University of SydneyHealth Canada
Mots-clésWifePhoneConversationImmigrationVisitor patternPsychologyMedicineLawPolitical scienceComputer science

Résumé

récupéré en direct d'OpenAlex

Soon after we were married, the time came for the normalization of my wife's immigration status. She was from Ecuador, and had a visitor's visa. By the time I got my act together enough to start working on her immigration papers, we were already a few months' pregnant. I had just begun my second year of medical school. As part of normalization, my wife needed an “immigration physical,” consisting of blood tests and vaccines that should be administered by “civil surgeons.” These exams are typically expensive, so we traveled far to the office of Dr. Delphin. His rates were reasonable, but his character proved to be exceptional. After a brief wait in his office, we entered the exam room. Dr. Delphin greeted us warmly, and he proceeded to get to know us. He wanted to know who we were, what we did, why we were there. Somewhere in the conversation, he learned that I was in my second year of medical school, and I expressed to him how I wanted to have a similar practice when I finished school. He told me that he would like to help and invited me to come back to his office the following Saturday, to help and learn. The next week flew by, and I became busy with schoolwork. That Saturday, I forgot to go to his office. I would not have remembered it at all if it weren't for a phone call I got that night. “José,” Dr. Delphin said, “I was waiting for you all day.” I felt horrible, apologized profusely, and asked for a new appointment. He gave me a second chance. Since the beginning Dr. Delphin was invested in my success. At our next meeting, Dr. Delphin taught me to draw blood. With his patients, I learned how to take blood efficiently. I went to his office regularly for the rest of medical school. Over time we became friends and our families got to know each other. We spent holidays together. Dr. Delphin became a trusted mentor. As I reflect back on this experience, I recognize my good fortune in finding a friend and mentor in Dr. Delphin. Those hours I spent in his office were filled with advice and wisdom, and it was a place to establish clinical relevance for the much-hated book work of my second year. He taught me to respect patients, that time was worth more than money, and that my time was the greatest gift that I could give my patients. When one of his patients unexpectedly died, he spent hours with the family, in his role as physician. He taught me to be one with the patients, and that each patient was a gift. Our relationship never would have developed if it weren't for Dr. Delphin's interest in me. I was an overwhelmed medical student, and he was a successful practicing physician. I needed a mentor, but I did not know it. He sensed my need and became my mentor, using techniques that he knew would engage me. His influence lives in me today. My choice of career in family medicine is due to his example. My commitment to the underserved is a direct result of his teachings. My work as full-time academic physician has roots in our relationship. I now have countless opportunities to mentor medical students. But more than anything, my efforts to become a good mentor are to repay him. He taught me true mentoring, where the mentor expects nothing in return. From him I learned that the best mentoring relationships are those that develop on their own—and for that I give profound thanks. José E. Rodriguez, MD Ronald Delphin,MD

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,016
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,144
Score d'incertitude au seuil0,482

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,016
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0180,008
Communication savante0,0150,009
Science ouverte0,0030,020
Intégrité de la recherche0,0040,013
Charge utile insuffisante (le modèle a refusé de juger)0,1440,072

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,037
Tête enseignante GPT0,370
Écart entre enseignants0,334 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2008
Routes d'admission1
Résumé présentoui

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